End-to-end large vocabulary speech recognition for the serbian language

Abstract

This paper presents the results of a large vocabulary speech recognition for the Serbian language, developed by using Eesen end-to-end framework. Eesen involves training a single deep recurrent neural network, containing a number of bidirectional long short-term memory layers, modeling the connection between the speech and a set of context-independent lexicon units. This approach reduces the amount of expert knowledge needed in order to develop other competitive speech recognition systems. The training is based on a connectionist temporal classification, while decoding allows the usage of weighted finite-state transducers. This provides much faster and more efficient decoding in comparison to other similar systems. A corpus of approximately 215 h of audio data (about 171 h of speech and 44 h of silence, or 243 male and 239 female speakers) was employed for the training (about 90%) and testing (about 10%) purposes. On a set of more than 120000 words, the word error rate of 14.68% and the character error rate of 3.68% is achieved.